JP2022033687A - Purchase price estimation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To highly accurately and automatically estimating purchase prices of products without any human intervention.

SOLUTION: A purchase price estimation program for estimating purchase prices of products, causes a computer to execute: information acquisition step of acquiring image information obtained by imaging the external appearance of a purchase object product; and an estimation step of estimating the purchase price of the purchase object product by using three- or more-stage relevance degree between reference image information obtained by imaging the external appearance of the product in the past, and a purchase price of the product and based on the reference image information tailored to the image information acquired in the information acquisition step.

SELECTED DRAWING: Figure 4

COPYRIGHT: (C)2022,JPO&INPIT

Description

The present invention relates to a purchase price estimation program and system suitable for estimating the purchase price of goods with high accuracy.

When you want to buy the goods you were using, you may want to know the appropriate price. In addition, the side that purchases the goods may also want to know the appropriate purchase price for the goods to be purchased.

This purchase price is a factor that should be referred to, such as not only the product name and brand of the item, but also how long it has been used, how many scratches it has, and the price of a general auction. There are tons of. Setting an appropriate purchase price while referring to each of these factors requires experience and knowledge, which is a difficult task and requires a great deal of labor.

For this reason, a system that can evaluate the purchase price of a product with high accuracy without relying on human hands has been conventionally desired.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is to be able to estimate the purchase price of goods with high accuracy and automatically without relying on human labor. To provide a purchase price estimation program and system.

The purchase price estimation program according to the present invention is an information acquisition step for acquiring image information of an image of the appearance of an article to be purchased in a purchase price estimation program for estimating the purchase price of an article, and an image of the appearance of the article in the past. Using the three or more levels of association between the reference image information and the purchase price of the goods, the purchase price of the goods to be purchased is determined based on the reference image information corresponding to the image information acquired in the information acquisition step. It is characterized by having a computer perform an estimation step to be estimated.

Anyone can easily estimate the purchase price of a product with high accuracy without any special skill or experience.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the purchase price estimation program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a purchase price estimation system 1 in which a purchase price estimation program to which the present invention is applied is implemented. The purchase price estimation system 1 includes an information acquisition unit 9, an estimation device 2 connected to the information acquisition unit 9, and a database 3 connected to the estimation device 2.

The information acquisition unit 9 is a device for a person who utilizes this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of taking an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the estimation device 2 described later. The information acquisition unit 9 outputs the detected information to the estimation device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be configured by a communication interface for acquiring data about the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the information acquisition unit 9 may be configured as a device for acquiring information such as drawings by scanning or reading from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects odors and scents.

The database 3 stores various information necessary for estimating the purchase price. The information necessary for estimating the purchase price is a reference image information that captures the appearance of the article in the past, or a reference consisting of the size or amount of scratches on the article extracted by imaging the article in the past. Appearance information, reference brand information regarding the brand of the article captured when acquiring the reference image information, reference product name information regarding the product name of the article captured when acquiring the reference image information, and reference image information. Reference usage years information regarding the number of years of use of the article captured when acquiring, reference winning bid value information regarding the winning bid value in other auctions of the article captured when acquiring the above reference image information, and the above reference image information. External environment information for reference regarding the external environment at the time of imaging when the article was imaged at the time of acquisition, reference market information regarding the market conditions at the time of imaging when the article was imaged at the time of acquiring the image information for reference, and image information for reference above. Reference text information written by the user to various information sites at the time of imaging when the article was imaged at the time of acquisition, reference support information regarding the support content of the manufacturer of the article imaged at the time of acquiring the above reference image information, The reference warranty information acquired by reading the warranty period and warranty details stated in the warranty card of the article captured when acquiring the reference image information, and the article imaged when acquiring the reference image information. A data set of reference containment information about the containment to be contained and the purchase price of the goods for which the actual judgment was made is stored.

That is, in the database 3, in addition to such reference image information, reference appearance information, reference brand information, reference product name information, reference usage years information, reference winning bid value information, reference external environment information, Any one or more of the reference market condition information, the reference text information, the reference support information, the reference guarantee information, and the reference containment information and the purchase price of the goods are stored in association with each other.

The estimation device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be the one to be converted. The user can obtain a search solution by this estimation device 2.

FIG. 2 shows a specific configuration example of the estimation device 2. The estimation device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire estimation device 2 and an operation unit 25 for inputting various control commands via an operation button, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the estimation device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 according to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technology.

The display unit 23 is configured by a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program will be read and executed by the control unit 24.

The operation in the purchase price estimation system 1 having the above-described configuration will be described. The following items include all items distributed at auctions, mail-order sales, over-the-counter sales, and pawn shops, including electrical products, leisure products, automobiles, interior / furniture, food, kitchen supplies, healthcare goods, jewelry, household goods, and tools. , Books, games, accessories, watches, etc.

The purchase price estimation system 1 estimates the purchase price when the purchaser wants the goods owned by the seller to be purchased by an auction, mail order, over-the-counter sales, pawn shop, etc., or when the purchaser purchases the goods owned by the seller. It is applied when you do. As shown in FIG. 3, for example, the purchase price estimation system 1 is premised on the premise that three or more levels of association between the reference image information and the purchase price of the article are set in advance. The reference image information is obtained from the image information obtained by imaging the article with respect to the appearance of the article, and can be obtained by analyzing the image information. This image may be a moving image as well as a still image. Further, although this image is composed of visible light, it may be composed of a so-called spectral image in which the display color is switched according to the spectrum, instead of the image. The reference image information may be used to identify the appearance of the article based on any of the size, shape, and color of the article by analyzing the image captured of the article. The appearance of these articles may be determined based on previously trained features. At this time, using artificial intelligence, image data such as the size, shape, and color of the article and the purchase price of the article are learned, and when actually acquiring the reference image information, these are learned. The purchase price of the item may be determined by comparing it with the image data.

In such a case, a prediction model machine-learned using the image information and the appearance of any of the size, shape, and color of the article as teacher data was used, the input was the image information, and the output was determined as the appearance. The appearance may be specified based on the result.

The purchase price of the goods is the actual purchase price of the goods captured as the reference image information. This purchase price may be obtained by accessing a database stored in an operating company such as a store, e-commerce, or a pawn shop, or may be manually input each time.
A data set consisting of such reference image information and the purchase price of the goods is acquired and learned.

In the example of FIG. 3, it is assumed that the input data is, for example, reference image information P01 to P03. The reference image information P01 to P03 as such input data is linked to the purchase price of the article as output. In this output, the purchase price of the goods as the output solution is displayed.

The reference image information is associated with each other through three or more levels of association with the purchase prices A to D of the goods as the output solution. Assuming that the goods are of the same type, the purchase price is, for example, 5000 yen for the purchase price A, 4000 yen for the purchase price B, 3500 yen for the purchase price C, and 3000 yen for the purchase price D. They are arranged like a circle. The reference image information is arranged on the left side through this degree of association, and the purchase price of each article is arranged on the right side through this degree of association. The degree of association indicates the degree to which the purchase price of the article is highly relevant to the reference image information arranged on the left side. In other words, this degree of association is an index showing what kind of goods the reference image information is likely to be associated with, and the purchase price of the most probable goods is selected from the reference image information. It shows the accuracy in doing so. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the purchase price of the goods as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The estimation device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the estimation device 2 accumulates a past data set as to which of the reference image information and the purchase price of the article in that case is adopted and evaluated in determining the actual search solution, and these By analyzing and analyzing, the degree of association shown in FIG. 3 is created.

For example, it is assumed that the reference image information is α in which the appearance of the article is evaluated by a numerical value in a plurality of stages such as size, shape, color, and degree of scratches. It is assumed that the purchase price A of the goods is highly evaluated as the purchase price of the goods for such reference image information. By collecting and analyzing such a data set, the degree of association with the reference image information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference image information P01, analysis is performed from various data as a result of evaluating the purchase price of the past goods. In the case of the reference image information P01, when there are many cases of the purchase price A of the goods, the degree of association leading to the evaluation of the purchase price of the goods is set higher, and there are many cases of the purchase price B of the goods. In some cases, the degree of association that leads to the evaluation of the purchase price of this item is set higher. For example, in the example of the reference image information P01, the purchase price A of the article and the purchase price C of the article are linked. The degree of association of w14, which leads to the purchase price C of, is set to 2 points.

Further, as shown in FIG. 4, this degree of association may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data through a data set of the image of the appearance of the article to be evaluated before and the purchase price of the actually estimated / evaluated article, the actual purchase price of the article will be newly determined from now on. In doing so, the purchase price of the goods will be searched for using the above-mentioned learned data. In such a case, the image information is newly acquired in the area to be actually discriminated. The newly acquired image information is input by the above-mentioned information acquisition unit 9. The image information is acquired by photographing the article for which the purchase price is to be determined. This determination method may be performed by the same method as the above-mentioned reference image information.

Based on the image information newly acquired in this way, the purchase price of the goods is determined. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02, the purchase price B of the article is associated with w15 and the purchase price C of the article is associated with the association degree w16 via the degree of association. There is. In such a case, the purchase price B of the article having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the purchase price C of the article having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, it is possible to search for the purchase price of the most suitable article from the newly acquired image information and display it on the selling side and the purchasing side. By seeing this search result, the selling side and the purchasing side can mutually determine the purchase price of the goods based on the purchase price of the searched goods. Moreover, since the purchase price of these items can be predicted through image capture without touching the item, even if the item is a valuable item or an antique item, it will not be damaged.

In FIG. 3, as an alternative to the reference image information, the reference appearance information consisting of either the size or the amount of scratches on the article extracted by imaging the article in the past and the purchase price of the article are shown. It is also possible to learn in advance the degree of association of three or more levels. When acquiring reference appearance information, as shown in FIG. 5, teacher data includes image data obtained by capturing the appearance of the article to be purchased and appearance information consisting of either the size or amount of scratches on the article. A machine-learned prediction model may be used as the data. The appearance information may be newly acquired by using the input as the image data and the output as the appearance information. That is, the appearance information consisting of either the size or the amount of the scratches may be discriminated based on the feature amount learned previously. The scratches referred to here include not only the scratches actually attached to the article but also the attached stains.

The appearance information for reference is constructed from the appearance information obtained in this manner, and the actual selling price (purchase price) of the imaged article is obtained to obtain a data set in the same manner as described above. The degree of association as shown in 4 is formed. Next, image data is acquired by taking an image of the item for which the purchase price is actually to be estimated. For this image data, the appearance information is obtained by actually referring to the degree of association shown in FIG. It is possible to search for the purchase price through the acquired appearance information by referring to the degree of association between the reference appearance information and the purchase price of the goods, which has been learned in advance.

In the example of FIG. 6, it is premised that a combination of the reference image information and the reference brand information is formed. The reference brand information is information indicating the manufacturer or seller of the article, or information about the brand of the trademark affixed to the article.

The purchase price of the goods varies depending on the brand of the goods in addition to the reference image information. In particular, the price of accessories greatly depends on whether or not they are famous brands. Therefore, this reference brand information is also added as an explanatory variable.

In the example of FIG. 6, it is assumed that the input data is, for example, reference image information P01 to P03 and reference brand information P14 to 17. The intermediate node shown in FIG. 6 is a combination of reference image information and reference brand information as such input data. Each intermediate node is further linked to the output. In this output, the purchase price of the goods as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference brand information is associated with each other through three or more levels of association with the purchase price of the goods as this output solution. The reference image information and the reference brand information are arranged on the left side through this degree of association, and the purchase price of the goods is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the purchase price of the goods with respect to the reference image information and the reference brand information arranged on the left side. In other words, this degree of association is an index indicating what kind of goods the reference image information and the reference brand information are likely to be associated with, and the reference image information and the reference brand information. It shows the accuracy in selecting the purchase price of the most probable goods from. Therefore, the optimum purchase price of the goods will be searched for by combining the reference image information and the reference brand information.

In the example of FIG. 6, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the estimation device 2 accumulates past data as to which of the reference image information, the reference brand information, and the purchase price of the article in that case is suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

For example, it is assumed that the reference image information in an actual case in the past is α. Further, it is assumed that the reference brand information is a certain famous brand W. In such a case, the purchase price of the goods, which indicates how much the purchase price of the goods was actually, is learned as a data set and defined in the form of the above-mentioned degree of association. In addition, such reference image information and reference brand information may be extracted from a management database managed by a seller, a distributor, or the like.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference brand information P16, the purchase price of the article is analyzed from the past data. When there are many cases where the purchase price of the goods is A, the degree of association leading to this purchase price A is set higher, and when there are many cases of the purchase price B of the goods and there are few cases of the purchase price A of the goods. , The degree of association leading to the purchase price B of the goods is set high, and the degree of association leading to the purchase price A of the goods is set low. For example, in the example of the intermediate node 61a, the output of the purchase price A and the purchase price B of the goods is linked. The degree of association of w14 connected to B is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node of the combination of the reference image information P01 and the reference brand information P14, the degree of association of the purchase price C of the goods is w15, and the purchase price of the goods. The degree of association of E is w16. The node 61c is a node of the combination of the reference position information P15 and P17 with respect to the reference image information P02, and the degree of association of the purchase price B of the article is w17 and the degree of association of the purchase price D of the article is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the purchase price of the goods from now on, the above-mentioned learned data will be used. In such a case, the image information and the brand information are actually acquired. For brand information, enter or select the purchase price of the ring for which the purchase price is actually to be estimated.

Based on the newly acquired image information and brand information in this way, the optimum purchase price of the goods is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the brand information is P17, the node 61d is associated via the degree of association, and this node. In 61d, the purchase price C of the article is associated with w19, and the purchase price D of the article is associated with the degree of association w20. In such a case, the purchase price C of the article having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the purchase price D of the article having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, an example of the degree of association w1 to w12 extending from the input is shown in Table 2 below.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

In FIG. 7, in addition to the above-mentioned reference image information, a combination with the reference product name information instead of the above-mentioned reference brand information and a degree of association of three or more levels with the purchase price of the goods for the combination are set. Here is an example.

This reference product name information, which is added as an explanatory variable instead of the reference position information, is related to the product name of the product, and can be classified through, for example, a model number, a product name, or the like. It is already known that the product name from the product is related to the purchase price of the product. Therefore, the purpose is to improve the accuracy of determining the purchase price of the goods by combining the reference goods name information to form the degree of association.

In the example of FIG. 7, it is assumed that the input data is, for example, reference image information P01 to P03 and reference product name information P18 to 21. The intermediate node shown in FIG. 7 is a combination of the reference product name information and the reference image information as such input data. Each intermediate node is further linked to the output. In this output, the purchase price of the goods as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference article name information is associated with each other through three or more levels of association with the purchase price of the article as this output solution. The reference image information and the reference product name information are arranged on the left side through this degree of association, and the purchase price of the goods is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the purchase price of the goods with respect to the reference image information and the reference goods name information arranged on the left side. In other words, this degree of association is an index indicating which reference image information and reference product name information are likely to be associated with the purchase price of what kind of goods, and the reference image information and reference product name information. It shows the accuracy in selecting the purchase price of the most probable goods from.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, in the estimation device 2, the reference image information, the reference product name information of the article captured when the reference image information is acquired, and the purchase price of the article in that case are used in determining the actual search solution. By accumulating past data and analyzing and analyzing which one was more suitable, the degree of association shown in FIG. 7 is created. The reference product name information may be obtained, for example, by capturing an image of a box or instruction manual in which the product name is described, or an article through a camera or the like, and converting the product name into text through OCR or the like.
This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference product name information P20, the purchase price of the product is analyzed from the past data. When there are many cases of the purchase price A of the goods, the degree of association that the purchase price of the goods leads to A is set higher, there are many cases where the purchase price of the goods is B, and the purchase price of the goods is A. When the number is small, the degree of association in which the purchase price of the goods is connected to B is set high, and the degree of association in which the purchase price of the goods is connected to A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the purchase price A of the goods and the purchase price B of the goods. The degree of association of w14 that leads to the purchase price B is set to 2 points.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node of the combination of the reference product name information P18 with respect to the reference image information P01, the degree of association of the purchase price C of the goods is w15, and the purchase price E of the goods. The degree of association is w16. The node 61c is a node of the combination of the reference goods name information P19 and P21 with respect to the reference image information P02, and the degree of association of the goods purchase price B is w17 and the degree of association of the goods purchase price D is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the purchase price of the goods from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the purchase price of the item and the item name information are actually acquired. Here, the product name information is newly acquired when actually estimating the purchase price of the product, and the acquisition method is the same as the reference product name information described above.

Based on the image information newly acquired in this way and the product name information, the optimum purchase price of the product is searched for. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the product name information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the purchase price C of the goods by w19 and the purchase price D of the goods by the degree of association w20. In such a case, the purchase price C of the article having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the purchase price D of the article having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In FIG. 8, in addition to the above-mentioned reference image information, a combination with the reference reference age information instead of the above-mentioned reference brand information and the purchase price of the goods for the combination are set to have three or more levels of association. An example is shown.

This reference years of use information added as this explanatory variable is any information about the years of use of the article. The reference years of use information may be obtained as the number of days calculated from the information on the production date and the sale date of the goods. Further, the production date and the sales date may be obtained by analyzing an image in which the production date and the sales date captured by the camera are described by OCR, or may be manually acquired.

In the example of FIG. 8, it is assumed that the input data is, for example, reference image information P01 to P03 and reference age information P18 to 21. The intermediate node shown in FIG. 8 is a combination of reference image information and reference years of use information as such input data. Each intermediate node is further linked to the output. In this output, the purchase price of the goods as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference years of use information is associated with each other through three or more levels of association with the purchase price of the goods as this output solution. The reference image information and the reference years of use information are arranged on the left side through this degree of association, and the purchase price of the goods is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the purchase price of the goods with respect to the reference image information and the reference years of use information arranged on the left side. In other words, this degree of association is an index showing which reference image information and reference usage years information are likely to be associated with the purchase price of what kind of goods, and is a reference image information and reference use. It shows the accuracy in selecting the most probable purchase price of goods from the age information.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the estimation device 2 determines the reference image information, the reference age information obtained when acquiring the reference image information, and the purchase price of the article in that case. Is suitable, or the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

For example, at the time of evaluating the purchase price of an actual article in the past, it is assumed that the reference image information has a reference usage period of 3 years. In such a case, if there are many cases where the purchase price of the goods is determined to be A, these are learned as a data set and defined in the form of the above-mentioned degree of association.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference years of use information P20, the purchase price of the article is analyzed from the past data. When there are many cases of the purchase price A of the goods, the degree of association that the purchase price of the goods leads to A is set higher, there are many cases where the purchase price of the goods is B, and the purchase price of the goods is A. When the number is small, the degree of association in which the purchase price of the goods is connected to B is set high, and the degree of association in which the purchase price of the goods is connected to A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the purchase price A of the goods and the purchase price B of the goods. The degree of association of w14 that leads to the purchase price B is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 8, the node 61b is a node of the combination of the reference image information P01 and the reference age information P18, the degree of association of the purchase price C of the article is w15, and the purchase price of the article. The degree of association of E is w16. The node 61c is a node in which the reference image information P02 is combined with the reference years of use information P19 and P21, and the degree of association of the purchase price B of the article is w17 and the degree of association of the purchase price D of the article is w18. It has become.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the purchase price of the goods from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the purchase price of the item and the information on the number of years of use are actually acquired. Here, the years of use information is newly acquired when actually estimating the purchase price of the goods, but the acquisition method is the same as the above-mentioned reference years of use information described above.

Based on the image information newly acquired in this way and the years of use information, the optimum purchase price of the goods is searched for. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the years of use information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the purchase price C of the goods by w19 and the purchase price D of the goods by the degree of association w20. In such a case, the purchase price C of the article having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the purchase price D of the article having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In FIG. 9, in addition to the above-mentioned reference image information, a combination with the reference winning bid price information instead of the above-mentioned reference brand information and a degree of association of three or more levels with the purchase price of the goods for the combination are set. An example is shown.

This reference winning bid information, which is added as an explanatory variable, is information about the winning bid value in another auction of the article imaged when the reference image information is acquired. Other auctions are assumed to be auctions operated on various sites published on the Internet. The winning bid price purchased at the highest price or a similar price through this auction is acquired and learned together with the reference image information.

In the example of FIG. 9, it is assumed that the input data is, for example, reference image information P01 to P03 and reference successful bid price information P18 to 21. The intermediate node shown in FIG. 9 is a combination of reference image information and reference successful bid price information as such input data. Each intermediate node is further linked to the output. In this output, the purchase price of the goods as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference successful bid price information is associated with each other through three or more levels of association with the purchase price of the goods as this output solution. The reference image information and the reference winning bid information are arranged on the left side through this degree of association, and the purchase price of the goods is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the purchase price of the goods with respect to the reference image information and the reference successful bid price information arranged on the left side. In other words, this degree of association is an index indicating what kind of goods the reference image information and the reference successful bid price information are likely to be associated with the purchase price of what kind of goods, and the reference image information and the reference successful bid. It shows the accuracy in selecting the most probable purchase price of goods from the value information.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the estimation device 2 determines which of the reference image information, the reference winning bid value information obtained when acquiring the reference image information, and the purchase price of the article in that case. Was suitable, or the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 9 is created.

For example, it is assumed that the reference successful bid price information is 500 yen for a certain reference image information at the time of evaluating the purchase price of the actual goods in the past. In such a case, if there are many cases where the purchase price of the goods is determined to be A, these are learned as a data set and defined in the form of the above-mentioned degree of association.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference successful bid price information P20, the purchase price of the article is analyzed from the past data. When there are many cases of the purchase price A of the goods, the degree of association that the purchase price of the goods leads to A is set higher, there are many cases where the purchase price of the goods is B, and the purchase price of the goods is A. When the number is small, the degree of association in which the purchase price of the goods is connected to B is set high, and the degree of association in which the purchase price of the goods is connected to A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the purchase price A of the goods and the purchase price B of the goods. The degree of association of w14 that leads to the purchase price B is set to 2 points.

Further, the degree of association shown in FIG. 9 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 9, the node 61b is a node of the combination of the reference image information P01 and the reference successful bid price information P18, the degree of association of the purchase price C of the article is w15, and the purchase price of the article. The degree of association of E is w16. The node 61c is a node of the combination of the reference successful bid price information P19 and P21 with respect to the reference image information P02, and the degree of association of the purchase price B of the article is w17 and the degree of association of the purchase price D of the article is w18. It has become.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the purchase price of the goods from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the purchase price of the item and the winning bid information are actually acquired. Here, the winning bid information is newly acquired when actually estimating the purchase price of the goods, but the acquisition method is the same as the above-mentioned reference winning bid information.

Based on the newly acquired image information and the winning bid price information in this way, the optimum purchase price of the goods is searched for. In such a case, the degree of association shown in FIG. 9 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the winning bid price information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the purchase price C of the goods by w19 and the purchase price D of the goods by the degree of association w20. In such a case, the purchase price C of the article having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the purchase price D of the article having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference external environment information instead of the above-mentioned reference brand information and the purchase price of the goods for the combination have three or more levels of association. May be used.

The reference external environment information and external environment information referred to here are, for example, economic data (GDP, employment statistics, mining and industry production index, capital investment, labor force survey, etc.), household data (household consumption status survey, household data, one week). Average working hours, savings amount statistical data, annual income statistical data, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market price, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation, etc.) It is represented by quantity data, wind direction data, humidity data, etc.). External environment information includes all external information other than those that reflect some or all of these data. The external environment information for reference may be categorized as the external environment itself. For example, they may be classified by separating them by data in employment statistics. Further, it may be categorized by a pattern (for example, a pattern in which the growth rate of GDP increases rapidly or gradually) or the like. This reference external environment information indicates the external environment during the past sales date or several days before and after that date.

The degree of association between the combination of the reference image information and the reference external environment information and the purchase price of the article is obtained in advance. Then, the image information regarding the appearance of the article to be estimated is newly acquired, and the external environment information regarding the external environment at the time of imaging (estimation) thereof is acquired. The point of estimating the purchase price of the goods based on the acquired reference image information according to the image information and the reference external environment information according to the external environment information is shown in FIGS. 3 to 9 described above. Same as the description.
Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference market condition information instead of the above-mentioned reference brand information and the purchase price of the goods for the combination have three or more levels of association. It may be used.

The reference market information and market information referred to here are various information related to the market conditions. Examples of this reference market information include interest rates, exchange rates, stock prices of each stock, crude oil, futures, precious metals, and price movements of Bitcoin. This reference market information may be displayed as a time-series chart, a line graph, or the like for these objects. Further, information such as Bollinger band, volume, MACD, moving average line and the like may be attached. In addition, this market condition information may be accompanied by information such as a chart of each brand, Bollinger band, MACD, and a moving average line. Information such as a chart showing price movements between currencies, Bollinger Bands, MACD, and moving averages may be attached to the foreign exchange.

A combination having such reference image information and reference market condition information, and a degree of association of three or more levels with the purchase price of the goods are acquired in advance. Then, the image information regarding the appearance of the article to be estimated is newly acquired, and the market condition information regarding the market condition at the time of imaging (estimation) is acquired. The point of estimating the purchase price of the goods based on the reference image information according to the acquired image information and the reference market information according to the market condition information is described in FIGS. 3 to 9 described above. The same is true.

Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference text information instead of the above-mentioned reference brand information and the purchase price of the goods for the combination have three or more levels of association. It may be used.

The reference text information and text information referred to here include all text data written to various information sites by purchasers and sellers of past goods and users who have used goods before. This reference text information also includes a note about the impression after using the article, how long the article lasted, the advantages and disadvantages of the article, and the like. That is, the reference text information includes any text information that may affect the purchase price for each item. The reference text information may simply be a prediction as to whether the purchase price of each item will rise, fall, or remain unchanged. Further, the reference text information may include comments by the general public posted on the Internet and forecasts of increase or decrease in the purchase price.

A combination having such reference image information and reference text information, and a degree of association of three or more levels with the purchase price of the article are acquired in advance. Then, in addition to the image information, the text information written in various information sites is newly acquired for the article to be estimated. The point of estimating the purchase price of the goods based on the reference image information corresponding to the acquired image information and the reference text information according to the text information is described in FIGS. 3 to 9 described above. The same is true.
Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference support information instead of the above-mentioned reference brand information and the purchase price of the goods for the combination have three or more levels of association. It may be used.

The reference support information and support information referred to here include all information regarding the support contents of the manufacturer of the goods. The reference support information and support information here are, for example, the support period of the item, the support content (for example, to what level the repair will be done, to what level the staff will support the usage, immediately after the staff comes. Information on whether or not they will respond). As a method of acquiring the support information for reference and the support information, a manual or a document containing the support contents may be imaged by a camera and the support contents may be read by using OCR technology. You may enter it manually.

The combination of such reference image information and reference support information, and the degree of association with the purchase price of the goods at three or more levels are acquired in advance. Then, image information regarding the appearance of the article to be estimated is newly acquired, and support information regarding the support content of the article is acquired. The point of estimating the purchase price of the goods based on the reference image information according to the acquired image information and the reference support information according to the support information is described in FIGS. 3 to 9 described above. The same is true.
Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference guarantee information instead of the above-mentioned reference brand information and the purchase price of the goods for the combination are associated with each other in three or more stages. It may be used.

The reference warranty information and warranty information referred to here include all information regarding the warranty content in the event that the article is damaged. The reference warranty information and warranty information referred to here are, for example, the warranty period of the product, the warranty content (in the case of a smartphone, if the touch panel is broken, is it supported up to the replacement, or more touch panel operation? If the sex has deteriorated, is the repair supported?). As a method of acquiring the warranty information and the warranty information for reference, a warranty card or a document or the like in which the warranty content is described may be imaged with a camera, and the warranty content may be read by using OCR technology. It may be input by input.

A combination having such reference image information and reference guarantee information, and a degree of association of three or more levels with the purchase price of the article are acquired in advance. Then, the image information regarding the appearance of the article to be estimated is newly acquired, and the guarantee information regarding the guarantee contents of the article is acquired. The point of estimating the purchase price of the goods based on the reference image information according to the acquired image information and the reference guarantee information according to the guarantee information is described in FIGS. 3 to 9 described above. The same is true.

Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference housing information instead of the above-mentioned reference brand information and the purchase price of the article for the combination have three or more levels of association. May be used.

The reference container information and the container information referred to here include information such as whether or not a container or box for accommodating the article is attached, and if so, the feeling of use, scratches, and the presence or absence of dirt. Is done. As a method of acquiring the guarantee information and the guarantee information for reference, the accommodation body may be imaged by a camera and automatically discriminated from the feature amount, or may be manually input.

The degree of association between the combination of the reference image information and the reference container information and the purchase price of the article is obtained in advance. Then, the image information regarding the appearance of the article to be estimated is newly acquired, and the container information regarding the container of the article is acquired. Then, the point of estimating the purchase price of the article based on the reference image information corresponding to the acquired image information and the reference container information according to the container information is shown in FIGS. 3 to 9 described above. Same as the description.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the purchase price of goods without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than that made by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the above-mentioned input data and output data are not completely the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

In the above-mentioned degree of association, in addition to the reference image information, the reference appearance information, the reference brand information, the reference product name information, the reference years of use information, the reference successful bid value information, the reference external environment information, and the reference The explanation has been given by taking as an example a combination of market information, reference text information, reference support information, reference warranty information, and reference containment information, but the explanation is not limited to this. .. In other words, in addition to the reference image information, the degree of association includes reference appearance information, reference brand information, reference product name information, reference years of use information, reference winning bid value information, reference external environment information, reference market information, etc. It may be composed of a combination of any two or more of the reference text information, the reference support information, the reference guarantee information, and the reference containment information. In addition to the reference image information, the degree of association includes reference appearance information, reference brand information, reference product name information, reference years of use information, reference winning bid value information, reference external environment information, and reference market information. In addition to any one or more of the reference text information, the reference support information, the reference guarantee information, and the reference containment information, other factors may be added to this combination to form the degree of association.
Further, in the above-mentioned example, the case where the reference image information is used as the keynote and this and other reference information are combined to form the degree of association has been described as an example, but the description is not limited to this, and the reference is made. The degree of association may be formed by combining the appearance information with other reference information. That is, in addition to the reference appearance information, the reference brand information, the reference product name information, the reference years of use information, the reference successful bid price information, the reference external environment information, the reference market information, the reference text information, and the reference support. The degree of association may be configured by a combination of any one or two or more of information, reference guarantee information, and reference containment information.

In either case, data is input according to the reference information of the degree of association, and the purchase price of the goods is obtained using the degree of association.

Further, according to the present invention, there is a feature that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by a numerical value from 0 to 100%, for example, in addition to the above-mentioned 10 levels, but is not limited to this, and any stage can be described as long as it can be described by a numerical value of 3 or more levels. It may be configured.

By determining the most probable purchase price of goods based on the degree of association expressed by such numerical values of three or more stages, in a situation where there are multiple possible candidates for a search solution, the degree of association can be determined. It is also possible to search and display in descending order. If the user can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. Remind the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided, for example, via a public communication network such as the Internet. In addition, reference image information, reference appearance information, reference brand information, reference product name information, reference usage years information, reference successful bid price information, reference external environment information, reference market information, reference text information, reference. When support information, reference guarantee information, and reference containment information are acquired, and knowledge, information, and data regarding the purchase price of goods for these and improvement measures are acquired, the degree of association is increased or decreased accordingly. Let me.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is done by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from the public communication network. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating a trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

When the degree of association shown in FIG. 3 is composed of the nodes of the neural network in artificial intelligence, as shown in FIG. 9, reference image information is input as input data, and the article is output data. The purchase price may be output, and at least one hidden layer may be provided between the input node and the output node for machine learning.

Further, as shown in FIG. 10, the present invention determines the purchase price of an article based on the degree of association between two or more types of information, reference information U and reference information V. The reference image information U is the reference water temperature, and the reference information V is the reference brand information, the reference product name information, the reference usage years information, the reference successful bid price information, the reference external environment information, and the reference market condition information. , Reference text information, reference support information, reference warranty information, reference containment information, etc.

At this time, as shown in FIG. 10, the output obtained for the reference information U is used as input data as it is, and is associated with the output (purchase price of the article) via the intermediate node 61 in combination with the reference information V. You may. For example, for reference information U (reference image information), after outputting an output solution as shown in FIG. 3, this is used as an input as it is, and the degree of association with other reference information V is used. The output (purchase price of goods) may be searched.

1 Purchase price estimation system 2 Estimator 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimator unit 28 Storage unit 61 Node

Claims (14)

In the purchase price estimation program that estimates the purchase price of goods
An information acquisition step to acquire image information that captures the appearance of the item to be purchased, and
The above is based on the reference image information corresponding to the image information acquired in the above information acquisition step by using the reference image information obtained by capturing the appearance of the article in the past and the degree of association between the purchase price of the article and the purchase price of the article in three or more stages. A purchase price estimation program characterized by having a computer perform an estimation step of estimating the purchase price of an item to be purchased. In the purchase price estimation program that estimates the purchase price of goods
An information acquisition step for acquiring appearance information consisting of either the size or amount of scratches on the extracted article by imaging the appearance of the article to be purchased, and
In the above information acquisition step, using the three or more levels of association between the reference appearance information consisting of either the size or amount of scratches on the article extracted by imaging the article in the past and the purchase price of the article. A purchase price estimation program characterized by causing a computer to perform an estimation step of estimating the purchase price of the above-mentioned goods to be purchased based on the appearance information for reference according to the acquired appearance information. In the above information acquisition step and the above estimation step, a prediction model machine-learned using image data obtained by capturing the appearance of the article to be purchased and appearance information consisting of either the size or amount of scratches on the article as teacher data is used. The purchase price estimation program according to claim 2, wherein the input is the image data and the output is the appearance information to newly acquire the appearance information. In the above information acquisition step, brand information regarding the brand of the goods to be estimated is acquired, and the brand information is acquired.
In the estimation step, the combination of the reference image information, the reference brand information regarding the brand of the article imaged when the reference image information is acquired, and the purchase price of the article are associated with each other in three or more stages. The purchase price estimation according to claim 1, characterized in that the purchase price of the goods to be purchased is estimated based on the reference brand information according to the brand information acquired in the above information acquisition step. program. In the above information acquisition step, the product name information regarding the product name of the product to be estimated is acquired, and the product name information is acquired.
In the estimation step, the combination having the reference image information, the reference product name information regarding the product name of the article imaged when the reference image information is acquired, and the purchase price of the article are associated with each other in three or more stages. The purchase price estimation according to claim 1, characterized in that the purchase price of the goods to be purchased is estimated based on the reference goods name information corresponding to the product name information acquired in the above information acquisition step. program. In the above information acquisition step, the service life information regarding the service life of the item to be estimated is acquired, and the information is acquired.
In the estimation step, there are three or more stages of the combination of the reference image information, the reference image information regarding the number of years of use of the article captured when the reference image information is acquired, and the purchase price of the article. 2. Purchase price estimation program. In the above information acquisition step, the winning bid information regarding the winning bid in other auctions of the item to be estimated is acquired.
In the estimation step, the combination having the reference image information and the reference successful bid price information regarding the successful bid price in another auction of the article imaged when the reference image information is acquired, and the purchase price of the article. A claim characterized by estimating the purchase price of the goods to be purchased based on the reference years of use information according to the years of use information acquired in the above information acquisition step, using the degree of association of three or more levels. Item 1 The purchase price estimation program described in Item 1. In the above information acquisition step, the market information regarding the market condition at the time of estimation and the external environment information regarding the external environment at the time of estimation are acquired.
In the estimation step, the reference image information, the reference market information regarding the market condition at the time of imaging when the article is imaged when the reference image information is acquired, and the article are imaged when the reference image information is acquired. For reference according to the market information acquired in the above information acquisition step, using the combination with the external environment information for reference regarding the external environment at the time of imaging and the degree of association with the purchase price of the above goods in three or more stages. The purchase price estimation program according to claim 1, wherein the purchase price of the above-mentioned goods to be purchased is estimated based on the market condition information and the external environment information for reference according to the external environment information. In the above information acquisition step, the market information regarding the market conditions at the time of estimation is acquired, and the market conditions are acquired.
In the estimation step, there are three stages: a combination having the reference image information, the reference market information regarding the market condition at the time of imaging when the article is imaged when the reference image information is acquired, and the purchase price of the article. The first aspect of claim 1, wherein the purchase price of the goods to be purchased is estimated based on the reference market information according to the market information acquired in the information acquisition step by using the above degree of association. Purchase price estimation program. In the above information acquisition step, the text information written on various information sites regarding the above-mentioned article and the item name information regarding the item name of the item to be estimated are acquired.
In the estimation step, the reference image information, the reference text information written to various information sites regarding the article obtained by capturing the reference image information, and the product name of the article captured when the reference image information is acquired. Using the combination with the reference product name information and the degree of association with the purchase price of the above goods in three or more stages, and further according to the reference text information and the product name information according to the text information acquired in the above information acquisition step. The purchase price estimation program according to claim 1, wherein the purchase price of the above-mentioned goods to be purchased is estimated based on the reference product name information. In the above information acquisition step, support information regarding the support content of the manufacturer of the product to be estimated and product name information regarding the product name of the product to be estimated are acquired.
In the estimation step, the reference image information, the reference support information regarding the support content of the manufacturer of the article captured when the reference image information is acquired, and the article captured when the reference image information is acquired. Reference support information and product name information according to the support information acquired in the above information acquisition step, using the combination with the reference product name information related to the product name and the degree of association with the purchase price of the above product in three or more stages. The purchase price estimation program according to claim 1, wherein the purchase price of the goods to be purchased is estimated based on the reference product name information according to the above. In the above information acquisition step, the warranty information obtained by reading the warranty period and the warranty contents described in the warranty card of the item to be estimated and the item name information regarding the item name of the item to be estimated are acquired.
In the estimation step, the reference image information, the reference warranty information obtained by reading the warranty period and the warranty content described in the warranty card of the article imaged when the reference image information is acquired, and the reference warranty information are described. Using the combination having the reference product name information regarding the product name of the article imaged when acquiring the reference image information and the degree of association with the purchase price of the above product in three or more stages, the information was further acquired in the above information acquisition step. The purchase price estimation program according to claim 1, wherein the purchase price of the above-mentioned goods to be purchased is estimated based on the reference guarantee information according to the guarantee information and the reference product name information according to the product name information. In the above information acquisition step, the containment information regarding the containment accommodating the item to be estimated and the item name information regarding the item name of the item to be estimated are acquired.
In the estimation step, the reference image information, the reference containment information regarding the containment accommodating the article captured when the reference image information was acquired, and the reference image information captured when the reference image information was acquired. Using the combination having the reference product name information regarding the product name of the article and the degree of association with the purchase price of the article in three or more stages, and further, the reference accommodation information according to the accommodation information acquired in the above information acquisition step. The purchase price estimation program according to claim 1, wherein the purchase price of the above-mentioned goods to be purchased is estimated based on the reference product name information according to the product name information. The purchase price estimation program according to any one of claims 1 to 13, wherein in the estimation step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used.

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